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Creating expressive, diverse and high-quality 3D avatars from highly customized text descriptions and pose guidance is a challenging task, due to the intricacy of modeling and texturing in 3D that ensure details and various styles…

Computer Vision and Pattern Recognition · Computer Science 2023-08-08 Huichao Zhang , Bowen Chen , Hao Yang , Liao Qu , Xu Wang , Li Chen , Chao Long , Feida Zhu , Kang Du , Min Zheng

Accurately predicting 3D occupancy grids from visual inputs is critical for autonomous driving, but current discriminative methods struggle with noisy data, incomplete observations, and the complex structures inherent in 3D scenes. In this…

Computer Vision and Pattern Recognition · Computer Science 2025-07-04 Yunshen Wang , Yicheng Liu , Tianyuan Yuan , Yingshi Liang , Xiuyu Yang , Honggang Zhang , Hang Zhao

Diffusion models have shown great promise for image generation, beating GANs in terms of generation diversity, with comparable image quality. However, their application to 3D shapes has been limited to point or voxel representations that…

Computer Vision and Pattern Recognition · Computer Science 2022-12-16 Gimin Nam , Mariem Khlifi , Andrew Rodriguez , Alberto Tono , Linqi Zhou , Paul Guerrero

Recent progress in 3D object generation has been fueled by the strong priors offered by diffusion models. However, existing models are tailored to specific tasks, accommodating only one modality at a time and necessitating retraining to…

Computer Vision and Pattern Recognition · Computer Science 2024-11-25 Yijun Fan , Yiwei Ma , Jiayi Ji , Xiaoshuai Sun , Rongrong Ji

Recent advancements in text-to-image generative systems have been largely driven by diffusion models. However, single-stage text-to-image diffusion models still face challenges, in terms of computational efficiency and the refinement of…

Computer Vision and Pattern Recognition · Computer Science 2024-03-11 Wendi Zheng , Jiayan Teng , Zhuoyi Yang , Weihan Wang , Jidong Chen , Xiaotao Gu , Yuxiao Dong , Ming Ding , Jie Tang

Recent 3D novel view synthesis (NVS) methods often require extensive 3D data for training, and also typically lack generalization beyond the training distribution. Moreover, they tend to be object centric and struggle with complex and…

Computer Vision and Pattern Recognition · Computer Science 2025-11-18 Taewon Kang , Divya Kothandaraman , Dinesh Manocha , Ming C. Lin

Large-scale diffusion models have achieved remarkable performance in generative tasks. Beyond their initial training applications, these models have proven their ability to function as versatile plug-and-play priors. For instance, 2D…

Computer Vision and Pattern Recognition · Computer Science 2025-02-21 Xiaofeng Yang , Cheng Chen , Xulei Yang , Fayao Liu , Guosheng Lin

Diffusion and flow matching models have unlocked unprecedented capabilities for creative content creation, such as interactive image and streaming video generation. The growing demand for higher resolutions, frame rates, and context…

Computer Vision and Pattern Recognition · Computer Science 2026-03-25 Brian Chao , Lior Yariv , Howard Xiao , Gordon Wetzstein

In this work, we introduce Unique3D, a novel image-to-3D framework for efficiently generating high-quality 3D meshes from single-view images, featuring state-of-the-art generation fidelity and strong generalizability. Previous methods based…

Computer Vision and Pattern Recognition · Computer Science 2024-10-29 Kailu Wu , Fangfu Liu , Zhihan Cai , Runjie Yan , Hanyang Wang , Yating Hu , Yueqi Duan , Kaisheng Ma

Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding excessive compute demands by training a diffusion model in a compressed lower-dimensional latent space. Here, we apply the LDM paradigm to high-resolution…

Computer Vision and Pattern Recognition · Computer Science 2023-12-29 Andreas Blattmann , Robin Rombach , Huan Ling , Tim Dockhorn , Seung Wook Kim , Sanja Fidler , Karsten Kreis

Text-to-image diffusion models pre-trained on billions of image-text pairs have recently enabled 3D content creation by optimizing a randomly initialized differentiable 3D representation with score distillation. However, the optimization…

Computer Vision and Pattern Recognition · Computer Science 2024-05-07 Yukun Huang , Jianan Wang , Yukai Shi , Boshi Tang , Xianbiao Qi , Lei Zhang

In recent years, the demand for 3D content has grown exponentially with the intelligent upgrade of interactive media, extended reality (XR), and Metaverse industries. In order to overcome the limitations of traditional manual modeling…

Graphics · Computer Science 2025-12-23 Xiang Tang , Ruotong Li , Xiaopeng Fan

The diffusion model is widely leveraged for either video generation or video editing. As each field has its task-specific problems, it is difficult to merely develop a single diffusion for completing both tasks simultaneously. Video…

Computer Vision and Pattern Recognition · Computer Science 2024-07-16 Haoyu Zhao , Tianyi Lu , Jiaxi Gu , Xing Zhang , Qingping Zheng , Zuxuan Wu , Hang Xu , Yu-Gang Jiang

Existing text-based 3D generation methods generate attractive results but lack detailed geometry control. Sketches, known for their conciseness and expressiveness, have contributed to intuitive 3D modeling but are confined to producing…

Graphics · Computer Science 2024-05-15 Feng-Lin Liu , Hongbo Fu , Yu-Kun Lai , Lin Gao

Large text-to-image diffusion models have impressive capabilities in generating photorealistic images from text prompts. How to effectively guide or control these powerful models to perform different downstream tasks becomes an important…

Computer Vision and Pattern Recognition · Computer Science 2024-03-15 Zeju Qiu , Weiyang Liu , Haiwen Feng , Yuxuan Xue , Yao Feng , Zhen Liu , Dan Zhang , Adrian Weller , Bernhard Schölkopf

Image-to-video generation, which aims to generate a video starting from a given reference image, has drawn great attention. Existing methods try to extend pre-trained text-guided image diffusion models to image-guided video generation…

Computer Vision and Pattern Recognition · Computer Science 2024-09-17 Cong Wang , Jiaxi Gu , Panwen Hu , Songcen Xu , Hang Xu , Xiaodan Liang

Existing single image-to-3D creation methods typically involve a two-stage process, first generating multi-view images, and then using these images for 3D reconstruction. However, training these two stages separately leads to significant…

Computer Vision and Pattern Recognition · Computer Science 2025-05-02 Hao Wen , Zehuan Huang , Yaohui Wang , Xinyuan Chen , Lu Sheng

Recent text-to-scene generation approaches largely reduced the manual efforts required to create 3D scenes. However, their focus is either to generate a scene layout or to generate objects, and few generate both. The generated scene layout…

Computer Vision and Pattern Recognition · Computer Science 2026-04-30 Zhenggang Tang , Yuehao Wang , Yuchen Fan , Jun-Kun Chen , Yu-Ying Yeh , Kihyuk Sohn , Zhangyang Wang , Qixing Huang , Alexander Schwing , Rakesh Ranjan , Dilin Wang , Zhicheng Yan

Recently, text-guided 3D generative methods have made remarkable advancements in producing high-quality textures and geometry, capitalizing on the proliferation of large vision-language and image diffusion models. However, existing methods…

Computer Vision and Pattern Recognition · Computer Science 2023-08-30 Xiao Han , Yukang Cao , Kai Han , Xiatian Zhu , Jiankang Deng , Yi-Zhe Song , Tao Xiang , Kwan-Yee K. Wong

Recent 3D large reconstruction models typically employ a two-stage process, including first generate multi-view images by a multi-view diffusion model, and then utilize a feed-forward model to reconstruct images to 3D content.However,…

Computer Vision and Pattern Recognition · Computer Science 2024-07-30 Zhenyu Tang , Junwu Zhang , Xinhua Cheng , Wangbo Yu , Chaoran Feng , Yatian Pang , Bin Lin , Li Yuan